1. Field of the Invention
The present invention relates to computers and computer networks. More particularly, the invention relates to analyze user activities in online social networks (OSNs).
2. Background of the Related Art
A social network is a social structure (e.g., community) made of members (e.g., a person) connected by social relationships such as friendship, kinship, relationships of beliefs, knowledge, prestige, culture, etc. Members of a social network often share interests and activities relating to such social relationships. For example, individual computers linked electronically could form the basis of computer mediated social interaction and networking within a social network community, referred to as an online social network (OSN). A social network service focuses on building online communities of people who share interests and/or activities, or who are interested in exploring the interests and activities of others. Most social network services are web based and provide a variety of ways (e.g., e-mail, instant messaging service, etc.) for users (or members) to interact socially.
Matching profiles of users across OSNs is a problem of great interest. Generally, only partial user profile information is available in a single OSN. Via the profile information overlap between different OSNs, profiles belonging to the same user can be concatenated to present a more complete profile, which can benefit personalize marketing, user online behavior analysis, etc. A number of previous works assess the feasibility of matching profiles across OSNs. These methods typically require large man/machine-hour to be practical or are restrictive in looking for matches. As a result, the growing size of today's information networks poses a scalability challenge to the schemes analyzing them. While the general similarity and distance measures such as edit distance and n-gram provides simple and clear ways to parse out the textual information for a small number of user profiles, the growing amount of string comparisons on networks with millions of profiles becomes a limiting factor for these methods. Further, even if the comparisons can be carried out somehow, the non-contextual, blind comparison leads to poor profile matching accuracy. For example, a comparison between user names, “Mary” and “Mark”, are considered very similar under edit distance measure while “Bill” and “William” are not.